When the brain is injured during development, there is an effect not only on immediate brain function but also on the future ability to learn and acquire skills. We propose to create and simulate multi-scale computational models of the effect of early structural injury on future motor function of the cortex and spinal cord. We will use programmable chips (FPGA's) and the new mathematical theory of Likelihood Calculus to build simulations of 300,000 neurons that run 500 times faster than real-time. The model will be fitted to electromyographic and kinematic data from children at two visits spaced one year apart, and predictions will be tested by comparison to a third visit one year later. High speed simulation of the effect of early injury has the potential to revolutionize the treatment of developmental neurological deficits because it can, in one week, simulate 10 years of future change. In doing so, it allows prediction of disease progression, prediction of the future effects of treatments, and detailed understanding of the interaction between brain injury and resulting disorders of movement, perception, and behavior. Because of these predictions, we can intervene much earlier with treatments customized to each patient's disease profile. With early intervention, it may be possible to attenuate or block the natural progression of their disease. With over 750,000 US children and adults affected by developmental brain disorders, early acquired brain injury, or childhood progressive brain disease, the applicability and potential impact of an early intervention and disease prediction technique are significant.